메뉴
Predict metagenome functional content from 16S rRNA marker gene data using PICRUSt2. Infer KEGG, MetaCyc, and EC abundances from ASV tables. Use when functional profiling is needed from 16S data without shotgun metagenomics sequencing.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/zja2004/BGI-CLI --skill bio-microbiome-functional-prediction명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | bio-microbiome-functional-prediction |
| description | Predict metagenome functional content from 16S rRNA marker gene data using PICRUSt2. Infer KEGG, MetaCyc, and EC abundances from ASV tables. Use when functional profiling is needed from 16S data without shotgun metagenomics sequencing. |
| tool_type | cli |
| primary_tool | picrust2 |
Reference examples tested with: Biostrings 2.70+, ggplot2 3.5+, pandas 2.2+, phyloseq 1.46+, scanpy 1.10+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturespackageVersion('<pkg>') then ?function_name to verify parameters<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Predict functional pathways from my 16S data" → Infer metagenome functional content from marker gene (16S/ITS) ASV tables using phylogenetic placement and gene content prediction.
picrust2_pipeline.py -s seqs.fna -i table.biom -o output/library(phyloseq)
library(Biostrings)
ps <- readRDS('phyloseq_object.rds')
# Export ASV table (samples as columns)
otu <- as.data.frame(otu_table(ps))
if (!taxa_are_rows(ps)) otu <- t(otu)
write.table(otu, 'asv_table.tsv', sep = '\t', quote = FALSE)
# Export ASV sequences as FASTA
seqs <- refseq(ps) # Or extract from ASV names if stored there
writeXStringSet(seqs, 'asv_seqs.fasta')
# Full pipeline (place sequences, predict functions, metagenome inference)
picrust2_pipeline.py \
-s asv_seqs.fasta \
-i asv_table.tsv \
-o picrust2_output \
-p 4 \
--stratified \
--per_sequence_contrib
# Output files:
# - pathway_abundance.tsv (MetaCyc pathways)
# - KO_metagenome_out/pred_metagenome_unstrat.tsv (KEGG orthologs)
# - EC_metagenome_out/pred_metagenome_unstrat.tsv (EC numbers)
Goal: Predict functional metagenome content from 16S ASVs using the full PICRUSt2 pipeline with explicit control over each step.
Approach: Place ASV sequences into a reference tree, predict gene content via hidden-state prediction, infer per-sample metagenome abundances, and reconstruct MetaCyc pathways.
# 1. Place sequences in reference tree
place_seqs.py -s asv_seqs.fasta -o placed_seqs.tre -p 4
# 2. Hidden state prediction (gene content)
hsp.py -i 16S -t placed_seqs.tre -o marker_nsti_predicted.tsv -m pic -n
# 3. Predict gene families (KO)
hsp.py -i KO -t placed_seqs.tre -o KO_predicted.tsv -m pic
# 4. Metagenome inference
metagenome_pipeline.py \
-i asv_table.tsv \
-m marker_nsti_predicted.tsv \
-f KO_predicted.tsv \
-o KO_metagenome_out \
--strat_out
# 5. Pathway inference
pathway_pipeline.py \
-i KO_metagenome_out/pred_metagenome_contrib.tsv \
-o pathway_output \
-p 4
import pandas as pd
# NSTI = Nearest Sequenced Taxon Index
# Lower = more reliable prediction (< 2 is acceptable)
nsti = pd.read_csv('marker_nsti_predicted.tsv', sep='\t')
print(f'Mean NSTI: {nsti["metadata_NSTI"].mean():.3f}')
print(f'ASVs with NSTI > 2: {(nsti["metadata_NSTI"] > 2).sum()}')
library(ggplot2)
pathways <- read.delim('picrust2_output/pathways_out/path_abun_unstrat.tsv', row.names = 1)
metadata <- read.csv('sample_metadata.csv', row.names = 1)
# Normalize to relative abundance
pathways_rel <- sweep(pathways, 2, colSums(pathways), '/')
# Differential pathway analysis (use ALDEx2 or similar)
library(ALDEx2)
groups <- metadata[colnames(pathways), 'Group']
pathway_aldex <- aldex(as.data.frame(t(pathways)), groups, mc.samples = 128)
# Map pathway IDs to names
add_descriptions.py \
-i pathway_abundance.tsv \
-m METACYC \
-o pathway_abundance_described.tsv
# Analyze KEGG modules instead of individual KOs
ko_table <- read.delim('KO_metagenome_out/pred_metagenome_unstrat.tsv', row.names = 1)
# Use KEGGREST for module mapping
library(KEGGREST)
modules <- keggLink('module', 'ko')